Current trends in Graz brain-computer interface (BCI) research

Pfurtscheller, Gert; Neuper, Christa; Guger, C.; Harkam, W.; Ramoser, Herbert; Schlögl, Alois; Obermaier, B.; Pregenzer, M.

doi:10.1109/86.847821

Cited by 440 publications

(229 citation statements)

References 20 publications

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“…The experimental approach was developed based on current understanding of sensorimotor rhythms and on the methodology of current EEG-based BCIs that use these rhythms [18] (see [1] for review). Sensorimotor rhythms comprise µ (8-12 Hz), β (18-26 Hz) and γ (>30 Hz) oscillations [6,[19][20][21][22]. They are thought to be produced by thalamocortical circuits and they change in amplitude in association with actual or imagined movements [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A brain–computer interface using electrocorticographic signals in humans

et al. 2004

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Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain. M This article features online multimedia enhancements

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Section: Introductionmentioning

confidence: 99%

A brain–computer interface using electrocorticographic signals in humans

et al. 2004

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“…Brain-computer interfaces (BCIs) are communication systems which enable users to send commands to computers using only their brain activity, which is generally measured by electroencephalography (EEG) [1,2]. BCI technology has been a promising tool for disabled people as well as for healthy people [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

Shi

2011

Neural Information Processing

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Abstract. Common spatial pattern (CSP) is very successful in constructing spatial filters for detecting event-related synchronization and event-related desynchronization. In statistics, a CSP filter can optimally separate the motor-imagery-related components. However, for a single trail, the EEG features extracted after a CSP filter still include features not related to motor imagery. In this study, we introduce a linear dynamical system (LDS) approach to motor-imagery-based brain-computer interface (MI-BCI) to reduce the influence of these unrelated EEG features. This study is conducted on a BCI competition data set, which comprises EEG signals from several subjects performing various movements. Experimental results show that our proposed algorithm with LDS performs better than a traditional algorithm on average. The results reveal a promising direction in the application of LDS-based approach to MI-BCI.

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“…v 2 is a part of x 2 , which is orthogonal to v 1 . In the same way, v k is a component of x k , which is orthogonal to all the previous orthogonal vectors v 1 …”

Section: Gram-schmidt Orthogonalizationmentioning

confidence: 99%

“…This set can be transferred into the orthogonal vector set {v 1 [18]. x 1 is used for v 1 . v 2 is a part of x 2 , which is orthogonal to v 1 .…”

Section: Gram-schmidt Orthogonalizationmentioning

confidence: 99%

“…Among the interfaces developed for the handicapped persons, Brain Computer Interface (BCI) has been attractive recently [1], [2]. Approaches to the BCI technology includes nonlinear classification by using spectrum power, adaptive auto-regressive model and linear classification, space patterns and linear classification, hidden Markov models, and so on [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Based BCI by Using Orthogonal Components of Multi-channel Brain Waves and Generalization

Nakayama

Horita

Hirano

Artificial Neural Networks - ICANN 2008

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Abstract. FFT and Multilayer neural networks (MLNN) have been applied to 'Brain Computer Interface' (BCI). In this paper, in order to extract features of mental tasks, individual feature of brain waves of each channel is emphasized. Since the brain wave in some interval can be regarded as a vector, Gram-Schmidt orthogonalization is applied for this purpose. There exists degree of freedom in the channel order to be orthogonalized. Effect of the channel order on classification accuracy is investigated. Next, two channel orders are used for generating the MLNN input data. Two kinds of methods using a single NN and double NNs are examined. Furthermore, a generalization method, adding small random numbers to the MLNN input data, is applied. Simulations are carried out by using the brain waves, available from the Colorado State University website. By using the orthogonal components, a correct classification rate Pc can be improved from 70% to 78%, an incorrect classification rate Pe can be suppressed from 10% to 8%. As a result, a rate Rc = Pc/(Pc + Pe) can be improved from 0.875 to 0.907. When two different channel orders are used, Pe can be drastically suppressed from 10% to 2%, and Rc can be improved up to 0.973. The generalization method is useful especially for using a sigle channel order. Pc can be increased up to 84 ∼ 88% and Pe can be suppressed down to 2 ∼ 4%, resulting in Rc = 0.957 ∼ 0.977.

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Current trends in Graz brain-computer interface (BCI) research

Cited by 440 publications

References 20 publications

A brain–computer interface using electrocorticographic signals in humans

A brain–computer interface using electrocorticographic signals in humans

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

Neural Network Based BCI by Using Orthogonal Components of Multi-channel Brain Waves and Generalization

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